Methods for objects of class "confint.rma" and "list.confint.rma".

# S3 method for confint.rma
as.data.frame(x, ...)
# S3 method for list.confint.rma
as.data.frame(x, ...)

Arguments

x

an object of class "confint.rma" or "list.confint.rma".

...

other arguments.

References

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03

Examples

### copy data into 'dat'
dat <- dat.bcg

### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat)

### fit random-effects model
res <- rma(yi, vi, data=dat)

### get 95% CI for tau^2, tau, I^2, and H^2
sav <- confint(res)
sav
#> 
#>        estimate   ci.lb   ci.ub 
#> tau^2    0.3132  0.1197  1.1115 
#> tau      0.5597  0.3460  1.0543 
#> I^2(%)  92.2214 81.9177 97.6781 
#> H^2     12.8558  5.5303 43.0680 
#> 

### turn object into a regular data frame
as.data.frame(sav)
#>          estimate      ci.lb     ci.ub
#> tau^2   0.3132433  0.1196953  1.111486
#> tau     0.5596815  0.3459701  1.054270
#> I^2(%) 92.2213861 81.9177227 97.678090
#> H^2    12.8557608  5.5302769 43.067984

############################################################################

### copy data into 'dat'
dat <- dat.berkey1998

### construct block diagonal var-cov matrix of the observed outcomes based on variables v1i and v2i
V <- vcalc(vi=1, cluster=author, rvars=c(v1i, v2i), data=dat)

### fit multivariate model
res <- rma.mv(yi, V, mods = ~ outcome - 1, random = ~ outcome | trial, struct="UN", data=dat)

### get 95% CI for variance components and correlation
sav <- confint(res)
sav
#> 
#>         estimate  ci.lb  ci.ub 
#> tau^2.1   0.0327 0.0095 0.2068 
#> tau.1     0.1807 0.0974 0.4547 
#> 
#>         estimate  ci.lb  ci.ub 
#> tau^2.2   0.0117 0.0001 0.1114 
#> tau.2     0.1083 0.0098 0.3338 
#> 
#>     estimate   ci.lb  ci.ub 
#> rho   0.6088 -0.5996 1.0000 
#> 

### turn object into a regular data frame
as.data.frame(sav)
#>           estimate         ci.lb     ci.ub
#> tau^2.1 0.03265144  9.489498e-03 0.2067776
#> tau.1   0.18069710  9.741405e-02 0.4547281
#> tau^2.2 0.01173314  9.597328e-05 0.1114429
#> tau.2   0.10831964  9.796596e-03 0.3338307
#> rho     0.60879624 -5.996186e-01 1.0000000